Dear Mahout Team, I am a student new to machine learning and i am trying to build a recommender using mahout.
My dataset is a csv file as an input but it has many fields as text and i understand mahout needs numeric values. First of all i would like your input as to what kind of recommender would best fit my dataset(I have considered many approaches such as neighbourhood methods,latency models etc) but i still am not sure what is best for my recommender. I looked up spark row similarity but i am not sure if it will suit my needs as i want to build my recommender as a java application with an interface. Secondly please explain how to calculate preference strength and do i need it. I have mainly implicit data about customer transaction history. The fields are as follows: customer id - numeric product id - text postal code - text sales - numeric product category - text shipping code - text potential growth - text territory - text Online Customer - Boolean Potential growth here suggests the strength what kind of future business can be build with a particular customer.(A is highest,E is lowest) Kindly contact me with your ideas and suggestions. Best Regards, Yash Patel
